OpenRaiser/Intern-Atlas huggingface dataset:
Optimality of binary power-control in a single cell via majorization
Venue: arXiv cs.IT 2011
Year: 2011

This paper considers the optimum single cell power-control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown, via the theory of majorization, that the optimum power allocation is binary, which means links are either "on" or "off". By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed, together with a reduced complexity near-optimal heuristic algorithm. Sufficient conditions under which channel-state aware time-division-multiple-access (TDMA) maximizes the aggregate communication rate are established. Finally, a numerical study is performed to compare and contrast the performance achieved by the optimum binary power-control policy with other sub-optimum policies and the throughput capacity achievable via successive decoding. It is observed that two dominant modes of communication arise, wideband or TDMA, and that successive decoding achieves better sum-rates only under near-perfect interference cancellation efficiency.
Ethane: A Heterogeneous Parallel Search Algorithm for Heterogeneous Platforms
Venue: arXiv cs.NE 2011
Year: 2011

In this paper we present Ethane, a parallel search algorithm specifically designed for its execution on heterogeneous hardware environments. With Ethane we propose an algorithm inspired in the structure of the chemical compound of the same name, implementing a heterogeneous island model based in the structure of its chemical bonds. We also propose a schema for describing a family of parallel heterogeneous metaheuristics inspired by the structure of hydrocarbons in Nature, HydroCM (HydroCarbon inspired Metaheuristics), establishing a resem- blance between atoms and computers, and between chemical bonds and communication links. Our goal is to gracefully match computers of different power to algorithms of different behavior (GA and SA in this study), all them collaborating to solve the same problem. The analysis will show that Ethane, though simple, can solve search problems in a faster and more robust way than well-known panmitic and distributed algorithms very popular in the literature.
Synchronous Control of Reconfiguration in Fractal Component-based Systems -- a Case Study
Venue: arXiv cs.SE 2011
Year: 2011

In the context of component-based embedded systems, the management of dynamic reconfiguration in adaptive systems is an increasingly important feature. The Fractal component-based framework, and its industrial instantiation MIND, provide for support for control operations in the lifecycle of components. Nevertheless, the use of complex and integrated architectures make the management of this reconfiguration operations difficult to handle by programmers. To address this issue, we propose to use Synchronous languages, which are a complete approach to the design of reactive systems, based on behavior models in the form of transition systems. Furthermore, the design of closed-loop reactive managers of reconfigurations can benefit from formal tools like Discrete Controller Synthesis. In this paper we describe an approach to concretely integrate synchronous reconfiguration managers in Fractal component-based systems. We describe how to model the state space of the control problem, and how to specify the control objectives. We describe the implementation of the resulting manager with the Fractal/Cecilia programming environment, taking advantage of the Comete distributed middleware. We illustrate and validate it with the case study of the Comanche HTTP server on a multi-core execution platform.
Optimum Sleep-Wake Scheduling of Sensors for Quickest Event Detection in Small Extent Wireless Sensor Networks
Venue: arXiv cs.NI 2011
Year: 2011

We consider the problem of quickest event detection with sleep-wake scheduling in small extent wireless sensor networks in which, at each time slot, each sensor node in the awake state observes a sample and communicates the information to the fusion centre. The sensor nodes in the sleep state do not sample or communicate any information to the fusion centre (FC), thereby conserving energy. At each time slot, the FC, after having received the samples from the sensor nodes in the wake state, makes a decision to stop (and thus declare that the event has occurred) or to continue observing. If it decides to continue, the FC also makes the decision of choosing the number of sensor nodes to be in the wake state in the next time slot. We consider three alternative approaches to the problem of choosing the number of sensor nodes to be in the wake state in time slot k+1, based on the information available at time slot k, namely, 1. optimal control of M_{k+1}, the number of sensor nodes to be in the awake state in time slot k+1, 2. optimal control of q_{k+1}, the probability of a sensor node to be in the awake state in time slot k+1, and 3. optimal probability q that a sensor node is in the awake state in any time slot. In each case, we formulate the problem as a sequential decision process. We show that a sufficient statistic for the decision at time k is the a posteriori probability of change Pi_k. Also, we show that the optimal stopping rule is a threshold rule on Pi_k. The optimal policy for M_{k+1} can keep very few sensors wake during the prechange phase and then quickly increase the number of sensors in the wake state when a change is "suspected". Among the three sleep-wake algorithms described, we observe that the total cost is minimum for the optimum control of M_{k+1} and is maximum for the optimum control on q.
Reasoning on Interval and Point-based Disjunctive Metric Constraints in Temporal Contexts
Venue: arXiv cs.AI 2011
Year: 2011

We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, where each disjunct in each input disjunctive constraint is univocally associated to a label. Reasoning algorithms manage labeled constraints, associated label lists, and sets of mutually inconsistent disjuncts. These algorithms guarantee consistency and obtain a minimal network. Additionally, constraints can be organized in a hierarchy of alternative temporal contexts. Therefore, we can reason on context-dependent disjunctive metric constraints on intervals and points. Moreover, the model is able to represent non-binary constraints, such that logical dependencies on disjuncts in constraints can be handled. The computational cost of reasoning algorithms is exponential in accordance with the underlying problem complexity, although some improvements are proposed.
Opportunistic Information Dissemination in Mobile Ad-hoc Networks: adaptiveness vs. obliviousness and randomization vs. determinism
Venue: arXiv cs.DS 2011
Year: 2011

In this paper the problem of information dissemination in Mobile Ad-hoc Networks (MANET) is studied. The problem is to disseminate a piece of information, initially held by a distinguished source node, to all nodes in a set defined by some predicate. We use a model of MANETs that is well suited for dynamic networks and opportunistic communication. In this model nodes are placed in a plane, in which they can move with bounded speed, and communication between nodes occurs over a collision-prone single channel. In this setup informed and uninformed nodes can be disconnected for some time (bounded by a parameter alpha), but eventually some uninformed node must become neighbor of an informed node and remain so for some time (bounded by a parameter beta). In addition, nodes can start at different times, and they can crash and recover. Under the above framework, we show negative and positive results for different types of randomized protocols, and we put those results in perspective with respect to previous deterministic results.
SENVM: Server Environment Monitoring and Controlling System for a Small Data Center Using Wireless Sensor Network
Venue: arXiv cs.NI 2011
Year: 2011

In recent years, efficient energy utilization becomes an essential requirement for data centers, especially in data centers of world-leading companies, where "Green Data Center" defines a new term for an environment-concerned data center. Solutions to change existing a data center to the green one may vary. In the big company, high-cost approaches including re-planning server rooms, changing air-conditioners, buying low-powered servers, and equipping sophisticating environmental control equipments are possible, but not for small to medium enterprises (SMEs) and academic sectors which have limited budget. In this paper, we propose a novel system, SENVM, used to monitor and control air temperature in a server room to be in appropriate condition, not too cold, where very unnecessary cooling leads to unnecessary extra electricity expenses, and also inefficient in energy utilization. With implementing on an emerging technology, Wireless Sensor Network (WSN), Green Data Center is feasible to every small data center with no wiring installation, easy deployment, and low maintenance fee. In addition, the prototype of the system has been tested, and the first phase of the project is deployed in a real-world data center.
Cloud-based Evolutionary Algorithms: An algorithmic study
Venue: arXiv cs.NE 2011
Year: 2011

After a proof of concept using Dropbox(tm), a free storage and synchronization service, showed that an evolutionary algorithm using several dissimilar computers connected via WiFi or Ethernet had a good scaling behavior in terms of evaluations per second, it remains to be proved whether that effect also translates to the algorithmic performance of the algorithm. In this paper we will check several different, and difficult, problems, and see what effects the automatic load-balancing and asynchrony have on the speed of resolution of problems.
Generating Functional Analysis of Iterative Algorithms for Compressed Sensing
Venue: arXiv cs.IT 2011
Year: 2011

It has been shown that approximate message passing algorithm is effective in reconstruction problems for compressed sensing. To evaluate dynamics of such an algorithm, the state evolution (SE) has been proposed. If an algorithm can cancel the correlation between the present messages and their past values, SE can accurately tract its dynamics via a simple one-dimensional map. In this paper, we focus on dynamics of algorithms which cannot cancel the correlation and evaluate it by the generating functional analysis (GFA), which allows us to study the dynamics by an exact way in the large system limit.
Handwritten Character Recognition of South Indian Scripts: A Review
Venue: arXiv cs.CV 2011
Year: 2011

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.
Randomized Algorithms for the Loop Cutset Problem
Venue: arXiv cs.AI 2011
Year: 2011

We show how to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized algorithm for finding a loop cutset outputs a minimum loop cutset after O(c 6^k kn) steps with probability at least 1 - (1 - 1/(6^k))^c6^k, where c > 1 is a constant specified by the user, k is the minimal size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm often finds a loop cutset that is closer to the minimum weight loop cutset than the ones found by the best deterministic algorithms known.
Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan
Venue: arXiv cs.AI 2011
Year: 2011

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.
Value-Function Approximations for Partially Observable Markov Decision Processes
Venue: arXiv cs.AI 2011
Year: 2011

Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price -- exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on efficient approximation (heuristic) methods that attempt to alleviate the computational problem and trade off accuracy for speed. We have two objectives here. First, we survey various approximation methods, analyze their properties and relations and provide some new insights into their differences. Second, we present a number of new approximation methods and novel refinements of existing techniques. The theoretical results are supported by experiments on a problem from the agent navigation domain.
On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note)
Venue: arXiv cs.AI 2011
Year: 2011

Pearl and Dechter (1996) claimed that the d-separation criterion for conditional independence in acyclic causal networks also applies to networks of discrete variables that have feedback cycles, provided that the variables of the system are uniquely determined by the random disturbances. I show by example that this is not true in general. Some condition stronger than uniqueness is needed, such as the existence of a causal dynamics guaranteed to lead to the unique solution.
Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes
Venue: arXiv cs.AI 2011
Year: 2011

Partially observable Markov decision processes (POMDPs) have recently become popular among many AI researchers because they serve as a natural model for planning under uncertainty. Value iteration is a well-known algorithm for finding optimal policies for POMDPs. It typically takes a large number of iterations to converge. This paper proposes a method for accelerating the convergence of value iteration. The method has been evaluated on an array of benchmark problems and was found to be very effective: It enabled value iteration to converge after only a few iterations on all the test problems.
Achievable Degrees of Freedom of the K-user Interference Channel with Partial Cooperation
Venue: arXiv cs.IT 2011
Year: 2011

In this paper, we consider the K-user interference channel with partial cooperation, where a strict subset of the K users cooperate. For the K-user interference channel with cooperating subsets of length M, the outer bound of the total degrees of freedom is KM/(M+1). In this paper, we propose a signal space-based interference alignment scheme that proves the achievability of these degrees of freedom for the case K=M+2. The proposed scheme consists of a design for the transmit precoding matrices and a processing algorithm which we call the Successive Interference Alignment (SIA) algorithm. The decoder of each message uses the SIA algorithm to process the signals received by the M cooperating receivers in order to get the maximum available degrees of freedom.
Slime mould computes planar shapes
Venue: arXiv cs.ET 2011
Year: 2011

Computing a polygon defining a set of planar points is a classical problem of modern computational geometry. In laboratory experiments we demonstrate that a concave hull, a connected alpha-shape without holes, of a finite planar set is approximated by slime mould Physarum polycephalum. We represent planar points with sources of long-distance attractants and short-distance repellents and inoculate a piece of plasmodium outside the data set. The plasmodium moves towards the data and envelops it by pronounced protoplasmic tubes.
A Note on Multiple-Access Channels with Strictly-Causal State Information
Venue: arXiv cs.IT 2011
Year: 2011

We propose a new inner bound on the capacity region of a memoryless multiple-access channel that is governed by a memoryless state that is known strictly causally to the encoders. The new inner bound contains the previous bounds, and we provide an example demonstrating that the inclusion can be strict. A variation on this example is then applied to the case where the channel is governed by two independent state sequences, where each transmitter knows one of the states strictly causally. The example proves that, as conjectured by Li et al., an inner bound that they derived for this scenario can indeed by strictly better than previous bounds.
Focalization and phase models for classical extensions of non-associative Lambek calculus
Venue: arXiv cs.LO 2011
Year: 2011

Lambek's non-associative syntactic calculus (NL) excels in its resource consciousness: the usual structural rules for weakening, contraction, exchange and even associativity are all dropped. Recently, there have been proposals for conservative extensions dispensing with NL's intuitionistic bias towards sequents with single conclusions: De Groote and Lamarche's classical non-associative Lambek calculus (CNL) and the Lambek-Grishin calculus (LG) of Moortgat and associates. We demonstrate Andreoli's focalization property for said proposals: a normalization result for Cut-free sequent derivations identifying to a large extent those differing only by trivial rule permutations. In doing so, we proceed from a `uniform' sequent presentation, deriving CNL from LG through the addition of structural rules. The normalization proof proceeds by the construction of syntactic phase models wherein every `truth' has a focused proof, similar to work of Okada and of Herbelin and Lee.
Reasoning within Fuzzy Description Logics
Venue: arXiv cs.AI 2011
Year: 2011

Description Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e., set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. In this paper we will present a fuzzy extension of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular, concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported. We will define its syntax, its semantics, describe its properties and present a constraint propagation calculus for reasoning in it.
Efficient Methods for Qualitative Spatial Reasoning
Venue: arXiv cs.AI 2011
Year: 2011

The theoretical properties of qualitative spatial reasoning in the RCC8 framework have been analyzed extensively. However, no empirical investigation has been made yet. Our experiments show that the adaption of the algorithms used for qualitative temporal reasoning can solve large RCC8 instances, even if they are in the phase transition region -- provided that one uses the maximal tractable subsets of RCC8 that have been identified by us. In particular, we demonstrate that the orthogonal combination of heuristic methods is successful in solving almost all apparently hard instances in the phase transition region up to a certain size in reasonable time.
Accelerating Reinforcement Learning through Implicit Imitation
Venue: arXiv cs.LG 2011
Year: 2011

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.
Short-term Performance Limits of MIMO Systems with Side Information at the Transmitter
Venue: arXiv cs.IT 2011
Year: 2011

The fundamental performance limits of space-time block code (STBC) designs when perfect channel information is available at the transmitter (CSIT) are studied in this report. With CSIT, the transmitter can perform various techniques such as rate adaption, power allocation, or beamforming. Previously, the exploration of these fundamental results assumed long-term constraints, for example, channel codes can have infinite decoding delay, and power or rate is normalized over infinite channel-uses. With long-term constraints, the transmitter can operate at the rate lower than the instantaneous mutual information and error-free transmission can be supported. In this report, we focus on the performance limits of short-term behavior for STBC systems. We assume that the system has block power constraint, block rate constraint, and finite decoding delay. With these constraints, although the transmitter can perform rate adaption, power control, or beamforming, we show that decoding-error is unavoidable. In the high SNR regime, the diversity gain is upperbounded by the product of the number of transmit antennas, receive antennas, and independent fading block channels that messages spread over. In other words, fading cannot be completely combatted with short-term constraints. The proof is based on a sphere-packing argument.